Monthly Archives: May 2017

Incorporating the concept of triangles in photos is an effective composition method used by professional photographers for making pictures more interesting or dynamic. Information on the locations of the embedded triangles is valuable for comparing the composition of portrait photos, which can be further leveraged by a retrieval system or used by photographers. This paper presents a system to automatically detect embedded triangles in portrait photos. The problem is challenging because the triangles used in portraits are often not clearly defined

by straight lines. The system first extracts a set of filtered line segments as candidate triangle sides, and then utilizes a modified RANSAC algorithm to fit triangles onto the set of line segments. We propose two metrics, Continuity Ratio and Total Ratio, to evaluate the fitted triangles; those with high fitting scores are taken as detected triangles. Experimental results have demonstrated high accuracy in locating preeminent triangles in portraits without dependence on the camera or lens parameters. To demonstrate the benefits of our method to digital photography, we have developed two novel applications that aim to help users composing high-quality photos. In the first application, we develop a human position and pose recommendation system by retrieving and presenting compositionally similar photos taken by competent photographers. The second application is a novel sketch-based triangle retrieval system which searches for photos containing specific triangular configuration. User studies have been conducted to validate the effectiveness of these approaches.

Severe weather conditions cause an enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive conditions such as damaging winds, hail, thunderstorms, and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions are bow echo with over 97% accuracy.

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.